MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Abstract
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation, which ignores the variations of modality preferences of different entities, thus compromising robustness against noise in modalities such as blurry images and relations. This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment. Experimental results demonstrate that our model not only achieves SOTA performance in multiple training scenarios, including supervised, unsupervised, iterative, and low-resource settings, but also has a limited number of parameters, efficient runtime, and interpretability. Our code is available at https://github.com/zjukg/MEAformer.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2022
- DOI:
- 10.48550/arXiv.2212.14454
- arXiv:
- arXiv:2212.14454
- Bibcode:
- 2022arXiv221214454C
- Keywords:
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- Computer Science - Artificial Intelligence;
- Computer Science - Computation and Language
- E-Print:
- ACM Multimedia 2023 Accpeted, Repo: https://github.com/zjukg/MEAformer